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Indexed by:期刊论文

Date of Publication:2012-10-01

Journal:KYBERNETES

Included Journals:SCIE、EI、Scopus

Volume:41

Issue:9

Page Number:1244-1251

ISSN No.:0368-492X

Key Words:Information networks; Network topology; Systems theory; Evolution model; Weighted network; Topological growth

Abstract:Purpose - The purpose of this paper is to study some evolving mechanisms for producing weighted networks, as well as to analyze the statistical properties of the networks.
   Design/methodology/approach - A simple one-parameter evolution model of weighted networks is proposed, in which the topological growth combines with the variation of weights. Based on weight-driven dynamics, the model can generate scale-free distributions of the degree, node strength and edge weight, as confirmed in many real networks.
   Findings - The exponent of the edge weight can be widely tuned. The unique parameter p controls the edge weight dynamical growth. The authors also obtain the non-trivial weighted clustering coefficient and the weighted average to the nearest neighbors' degree.
   Research limitations/implications - Accessibility and availability of data are the main limitations which apply to the figures.
   Practical implications - The new evolving networks method may be beneficial for understanding real networks.
   Originality/value - The paper proposes a new approach of explaining the evolving mechanisms of the real networks.